Artificial Neural Networks Report
Artificial Neural Networks
1. Introduction
Artificial Neural Networks are computational models inspired by an animal's central nervous systems (brain) that has the ability of machine learning. Artificial neural networks are generally presented as systems of interconnected "neurons" which can compute values from inputs (from wikipedia).
2. Training an Artificial Neural Network
The network is ready to be trained if it had been structured to service a particular application, meanwhile the initial weights are chosen randomly and after that the training begins.
There are two approaches in training Artificial Neural Networks: supervised and unsupervised.
2.1 Supervised Training
In supervised training, with a teacher, so we notice that both the inputs and the outputs are provided, compares its outputs result with the desired outputs. .
2.2 Unsupervised Training
The unsupervised training, without a teacher, so we see in the unsupervised training, the network is provided with inputs but without desired outputs. So system itself must then decide what features it will use to group or classify (clustering) the input data. This is often referred to as self-organization.
3. Some Issues In Neural networks
3.1Number of input nodes
Input sets are dynamic.Number of input is equal to number of features (columns), once we know the shape of our training data we can the input number ,some the methods like Sensitivity Based Pruning, Average Absolute Derivate Magnitude and others can be used to determine the input neuron numbers.
3.2Number of Output nodes
Output sets are dynamic. The number of output neurons is calculated by the chosen model configuration. The outpu...
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In order for us to fully understand why we cannot achieve real learning one must understand the roots as well as the problem. Twenge talks about the problem ...
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Clustering This is un-supervised learning method. Text documents here are unlabelled and inherent patterns in text are revealed through cluster formation. This can also be used as prior step for other text mining methods.
Stergiou, C., & Siganos, D. (2011, August 6). Neural Networks. Retrieved August 6, 2011, from
Let us now see the quality of individual the population over the time. As shown below at the starting point of the algorithm individuals are of less quality. However as the time goes by population’s individuals are getting of higher quality and reaching the pick of global and local optima. The image below illustrate these stages of the algorithm.
The number of synaptic inputs recieved by each nerve cell in our (human) nervous system varies from 1-100,000! This wide range reflects the fundamental purpose of nerve cells, to integrate info from other neurons.
Learning in its most basic form is our minds associating one thing with another. Digging deeper reveals that there are trends in how human beings and animals learn by association, usually this is done by a brain connecting one event to another. The two different ways a brain tends to learn is through either classical conditioning or operant conditioning. Classical conditioning is learning to associate one stimulus with another stimulus, and Operant Conditioning is learning by associating a response or behavior with a consequence. Knowing how people and animals learn is an important piece of knowledge if one is to help benefit the greater good.
then replicating the behavior that was observed. Observational learning is an important area inthe field of psychology because according to www.ncbi.nlm.nin.gov research in observational learning represents a critical development in the history of psychology. There are many learningtheories such as classical conditioning and operant conditioning which emphasize how direct experiences, reinforcements, and punishment lead to learning, but most learning happens indirectly by watching and imitating others. Observational learning is also referred to as shaping, modeling,
Then classification is performed on the basis of similarity score of a class with respect to a neighbor.
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Singh, Y. & Chauhan, A. (2009). Neural networks in data mining. Journal of Theoretical and
Artificial intelligence is defined as developing computer programs to solve complex problems by applications of processes that are analogous to human reasoning processes. Roughly speaking, a computer is intelligent
When we say that the machine learns, we mean that the machine is able to make predictions from examples of desired behavior or past observations and information. More formal definition of machine learning by Tom Mitchell is A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E. The definition also indicates the main goal of machine learning: the design of such programs
In order for a computer to do the desirable task, it has to receive a code, which is a set of orders that gives details about what needs to be done. The codes are developed and formulated to be able to answer problems the computer is meant to solve. In a human, the codes in a computer are similar to the nerve pulses. In a human, the nerve impulses appear in a specific sequence as well as on a specific axon. It is not a random task, but it is rather structured to make sure the nerve pulses are working